ggplot2 basics

Data Visualization

Javier Tamayo-Leiva

22 December 2020

ggplot2 basics

Index

ggplot2 Package
Basic Plot
Layers
Aesthetic Mappings
Geom Objects
Statistical Transformation
Position Adjustments
Extra Settings

Tip

The slides are based on practical learning and the philosophy of free knowledge. To do so, all the command lines are functional and thought to be copied and pasted into your R session. The dataset required is already loaded into the ggplot2 package. Throughout each plot’s completeness, you will also gain basic knowledge of R language, ggplot2, and data visualization.

ggplot2-package

# Install from CRAN
install.packages("tidyverse")

# Or the development version from GitHub
# install.packages("devtools")
devtools::install_github("tidyverse/tidyverse")

# or just ggplot2
install.packages("ggplot2")

# Load library
library(tidyverse)
library(ggplot2)

You can load ggplot2 with tidyverse or independently into your R session.
For more info, you can type into your R session
help(“ggplot2”)

The Dataset (ggplot2::diamonds)

Prices of over 50,000 round cut diamonds.

head(diamonds)

## # A tibble: 6 x 10
##   carat cut       color clarity depth table price     x     y     z
##   <dbl> <ord>     <ord> <ord>   <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.23  Ideal     E     SI2      61.5    55   326  3.95  3.98  2.43
## 2 0.21  Premium   E     SI1      59.8    61   326  3.89  3.84  2.31
## 3 0.23  Good      E     VS1      56.9    65   327  4.05  4.07  2.31
## 4 0.290 Premium   I     VS2      62.4    58   334  4.2   4.23  2.63
## 5 0.31  Good      J     SI2      63.3    58   335  4.34  4.35  2.75
## 6 0.24  Very Good J     VVS2     62.8    57   336  3.94  3.96  2.48

dim(diamonds)

## [1] 53940    10

Basic plot

ggplot(data= diamonds, mapping= aes(x= carat, y= price)) + geom_point()

price : price in US dollars ($326–$18,823)
carat : weight of the diamond (0.2–5.01)

Basic command

ggplot(data= diamonds, mapping= aes(x= carat, y= price))

data : Default dataset to use for plot (diamonds)
mapping : Default list of aesthetic mappings (visual properties) to use for plot (x= carat, y= price). If not specified, must be supplied in each layer added to the plot

ggplot2 layers

A layer combines data, aesthetic mapping, geom (geometric object), stat (statistical transformation), and a position adjustment. Rendering a layer, you decide the type of ggplot2 object to create. However, the layers you can apply will depend on your data and geom object you select.

Layers can also combine different datasets into one unique ggplot2 object. This feature allows you to create sophisticated plots.

You can apply the officials’ layers that are already loaded into ggplot2 packages or used ggplot2 extensions, which are into other R packages.

ggplot2 Aesthetic mappings

Required aesthetic

X
Y (Two variables)

Extra aesthetic

Color or Colour
Fill
Size
Shape
Linetype
Alpha (transparency)
Group
Label
Stroke…

Learn more about these options by typing in your R session.
vignette(“ggplot2-specs”)

Plot Aesthetics

ggplot(data= diamonds, mapping= aes(x= carat, y= price, colour= color)) + 
  geom_point()

Plot Aesthetics variables

ggplot(data=diamonds,mapping=aes(x=carat,y=price,colour=color,size=table)) + 
  geom_point()

Geom Objects

One variable

geom_bar()
geom_histogram()
geom_density()
…

Two variables

geom_point()
geom_line()
geom_smooth()
…

Three variables

geom_contour()
geom_tile()
geom_raster()

Display error

geom_crossbar()
geom_errorbar()
geom_linerange()
geom_pointrange()

Plot geoms (Two variables)

geom_line()

ggplot(data=diamonds,
       mapping=aes(x=carat,y=price,colour=color)) +
  geom_line()

geom_smooth()

ggplot(data=diamonds,
       mapping=aes(x=carat,y=price,colour=color)) +
  geom_smooth()

Plot geoms (One and Three variables)

geom_density()

ggplot(data=diamonds,
       mapping=aes(x=carat,colour=color)) +
  geom_density()

geom_tile()

ggplot(data=diamonds,
       mapping=aes(x=carat,y=price,fill=color)) +
  geom_tile()

Plot joined layers

ggplot(data=diamonds,mapping=aes(x=carat,y=price,colour=color)) + 
  geom_point() +
  geom_smooth()

Statistical Transformation

Stat

stat_count()
stat_bin_2d()
stat_boxplot()
stat_contour()
stat_contour_filled()
stat_density()
stat_bin()
stat_qq()

Geom

geom_bar()
geom_bin2d()
geom_boxplot()
geom_contour()
geom_contour_filled()
geom_density()
geom_histogram()
geom_qq()

Statistical Transformation Options

Statistical Transformation

geom_bar()

ggplot(data=diamonds,
       mapping=aes(x=cut,fill=color)) +
  geom_bar()

stat_count()

ggplot(data=diamonds,
       mapping=aes(x=cut,fill=color)) +
  stat_count()

Statistical Transformation

stat_density()

ggplot(data=diamonds,
       mapping=aes(x=cut,fill=color)) +
  stat_density()

geom_density()

ggplot(data=diamonds,
       mapping=aes(x=cut,fill=color)) +
  geom_density()

Tip

Consider the differential settings between geoms and stat elements

stat_density(
mapping = NULL,
data = NULL,
geom = “area”,
position = “stack”,
…)

geom_density(
mapping = NULL,
data = NULL,
stat = “density”,
position = “identity”,
…)

Position Adjustments

Position

position_dodge()
position_dodge2()
position_identity()
position_jitter()
position_jitterdodge()
position_stack()
position_fill()
position_nudge()

Geom

“dodge”
“dodge2”
“identity”
“jitter”
position_jitterdodge()
“stack” (counts)
“fill” (proportion)
nudge_x, nudge_y (text)

Position Adjustments Options

Position Adjustments

position_fill()

ggplot(data=diamonds,
       mapping=aes(x=cut,fill=color)) +
  geom_bar(position = "fill")

position_dodge2()

ggplot(data=diamonds,
       mapping=aes(x=cut,fill=color)) +
  geom_bar(position = "dodge2")

ggplot2 Extra settings

Coordinate systems

facet_wrap()
facet_grid()

theme()

color scales

Coordinate systems

coord_flip()

ggplot(data=diamonds,
       mapping=aes(x=cut,fill=color)) +
  geom_bar(position = "dodge2") +
  coord_flip()

coord_polar()

ggplot(data=diamonds,
       mapping=aes(x=cut,fill=color)) +
  stat_density() +
  coord_polar()

ggplot(data=diamonds,
       mapping=aes(x=cut,fill=color)) +
  geom_bar(position = "dodge2") +
  coord_polar()

Plot multi-panel

ggplot(data=diamonds,mapping=aes(x=carat,y=price,colour=color)) + geom_point() + geom_smooth() +
  facet_wrap(vars(color))

Plot multi-panel

ggplot(data=diamonds,mapping=aes(x=carat,y=price,colour=color)) + geom_point() + geom_smooth() +
  facet_wrap(vars(color, clarity))

Plot multi-panel

ggplot(data=diamonds,mapping=aes(x=carat,y=price,colour=color)) + geom_point() +
  facet_grid(cut ~ clarity)

Plot multi-panel

ggplot(data=diamonds,mapping=aes(x=carat,y=price,colour=color)) + geom_point() +
  facet_grid(rows = vars(cut))

Plot multi-panel

ggplot(data=diamonds,mapping=aes(x=carat,y=price,colour=color)) + geom_point() +
  facet_grid(cols = vars(clarity))

Plot multi-panel

ggplot(data=diamonds,mapping=aes(x=carat,y=price,colour=color)) + geom_point() +
  facet_grid(vars(cut), vars(clarity))

ggplot2 themes

theme_grey() (default)
theme_minimal()
theme_classic()
theme_bw()

theme_linedraw()
theme_light()
theme_dark()
theme_void()

You can also manually modify components of a theme. For more info look at theme()

Plot theme

theme_grey() (default)

ggplot(data=diamonds,
       mapping=aes(x=carat,y=price,colour=color)) +
  geom_smooth() + theme_grey()

theme_minimal()

ggplot(data=diamonds,
       mapping=aes(x=carat,y=price,colour=color)) +
  geom_smooth() + theme_minimal()

Plot theme

theme_classic()

ggplot(data=diamonds,
       mapping=aes(x=carat,y=price,colour=color)) +
  geom_smooth() + theme_classic()

theme_bw()

ggplot(data=diamonds,
       mapping=aes(x=carat,y=price,colour=color)) +
  geom_smooth() + theme_bw()

Plot theme

theme_linedraw()

ggplot(data=diamonds,
       mapping=aes(x=carat,y=price,colour=color)) +
  geom_smooth() + theme_linedraw()

theme_light()

ggplot(data=diamonds,
       mapping=aes(x=carat,y=price,colour=color)) +
  geom_smooth() + theme_light()

Plot theme

theme_dark()

ggplot(data=diamonds,
       mapping=aes(x=carat,y=price,colour=color)) +
  geom_smooth() + theme_dark()

theme_void()

ggplot(data=diamonds,
       mapping=aes(x=carat,y=price,colour=color)) +
  geom_smooth() + theme_void()

ggplot2 color

RColorBrewer

scale_colour_brewer()
scale_fill_brewer()
scale_colour_distiller()
scale_fill_distiller()
scale_colour_fermenter()
scale_fill_fermenter()

Viridis

scale_colour_viridis_d()
scale_fill_viridis_d()
scale_colour_viridis_c()
scale_fill_viridis_c()
scale_colour_viridis_b()
scale_fill_viridis_b()

ggplot2 color

scale_colour_brewer()

ggplot(data=diamonds,
       mapping=aes(x=carat,y=price,colour=color)) +
  geom_point() + 
  scale_colour_brewer(palette = "Set1")

scale_colour_viridis_d()

ggplot(data=diamonds,
       mapping=aes(x=carat,y=price,colour=color)) +
  geom_point() + 
  scale_colour_viridis_d(option = "inferno")

ggplot2 color

scale_colour_brewer()

ggplot(data=diamonds,
       mapping=aes(x=carat,y=price,colour=color)) +
  geom_point() + 
  scale_colour_brewer(palette = "Set2")

scale_colour_viridis_d()

ggplot(data=diamonds,
       mapping=aes(x=carat,y=price,colour=color)) +
  geom_point() + 
  scale_colour_viridis_d(option = "plasma")

ggplot2 color

scale_colour_brewer()

ggplot(data=diamonds,
       mapping=aes(x=carat,y=price,colour=color)) +
  geom_point() + 
  scale_colour_brewer(palette = "Set3")

scale_colour_viridis_d()

ggplot(data=diamonds,
       mapping=aes(x=carat,y=price,colour=color)) +
  geom_point() + 
  scale_colour_viridis_d(option = "magma")

ggplot2 color

scale_colour_brewer()

ggplot(data=diamonds,
       mapping=aes(x=carat,y=price,colour=color)) +
  geom_point() + 
  scale_colour_brewer(palette = "Paired")

scale_colour_viridis_d()

ggplot(data=diamonds,
       mapping=aes(x=carat,y=price,colour=color)) +
  geom_point() + 
  scale_colour_viridis_d(option = "viridis")

ggplot2 color

scale_colour_brewer()

ggplot(data=diamonds,
       mapping=aes(x=carat,y=price,colour=color)) +
  geom_point() + 
  scale_colour_brewer(palette = "Dark2")

scale_colour_viridis_d()

ggplot(data=diamonds,
       mapping=aes(x=carat,y=price,colour=color)) +
  geom_point() + 
  scale_colour_viridis_d(option = "cividis")

ggplot2 set scale color manual

Set colours

cols <- c("#1f77b4", "#aec7e8", "#ff7f0e", "#ffbb78", "#2ca02c", "#98df8a", "#d62728")

ggplot(data=diamonds,
       mapping=aes(x=carat,y=price,colour=color)) +
  geom_point() +
  scale_colour_manual(values = cols)

Set colours and order

cols <- c("J"="#1f77b4", "I"="#aec7e8", "H"="#ff7f0e", "G"="#ffbb78", "F"="#2ca02c", "E"="#98df8a", "D"="#d62728")

ggplot(data=diamonds,
       mapping=aes(x=carat,y=price,colour=color)) +
  geom_point() + 
  scale_colour_manual(values = cols)

“Less is more!”

― Ludwig Mies Van Der Rohe.

(don’t overplot)

Overplot

ggplot(data=diamonds,mapping=aes(x=carat,y=price,colour=color,shape=cut,linetype=color)) + 
  geom_point() + geom_line() + geom_smooth() + geom_rug()

R Handbooks

R Language

Hands-On Programming with R
R for Data Science
Data Science at the Command Line

Visualization

ggplot2
Data Visualization
The R Graph Gallery
Fundamentals of Data Visualization

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